A Method of Soil Salinization Information Extraction with SVM Classification Based on ICA and Texture Features  

作　　者：ZHANG Fei TASHPOLAT Tiyip KUNG Hsiang-te DING Jian-li MAMAT.Sawut VERNER Johnson HAN Gui-hong GUI Dong-wei 

机构地区：[1]College of Resources and Environment Science,Xinjiang University,Urumqi 830046 [2]Key Laboratory of Oasis Ecology,Xinjiang University,Urumqi 830046 [3]Department of Earth Sciences,the University of Memphis,Memphis,TN 38152,USA [4]Graduate School of Xinjiang University,Urumqi 830046 [5]Department of Physical and Environmental Sciences Mesa State College in Grand Junction,CO 81501,USA [6]Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi 830011 [7]Cele National Station of Observation ＆ Research for Desert-Grassland Ecosystem in Xinjiang,Cele 848300

出　　处：《Agricultural Science & Technology》2011年第7期1046-1049,1074,共5页农业科学与技术（英文版）

基　　金：Supported by the National Key Basic Research Development Pro-gram (2009CB421302 );National Natural Science Foundation ofChina (40861020,40961025,40901163);Natural Science Foun-dation of Xinjiang (200821128 );Open Foundation of State KeyLaboratory of Resources and Environment Information ystems(2010KF0003SA)

摘　　要：Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM +Landsat data.It reports the Support Vector Machine(SVM) classification method based on Independent Component Analysis(ICA) and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification,single data source SVM classification,maximum likelihood classification(MLC) and neural network classification qualitatively and quantitatively.The result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 98.64%,which increases by10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM＋ Landsat data.It reports the Support Vector Machine（SVM） classification method based on Independent Component Analysis（ICA） and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification,single data source SVM classification,maximum likelihood classification（MLC） and neural network classification qualitatively and quantitatively.The result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 98.64%,which increases by 10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.

关 键 词：Independent component analysis（ICA） Texture features Support vector machine（SVM） Soil salinizaiton 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] S156.4[自动化与计算机技术—控制科学与工程]